One species in eight: DNA barcodes from type specimensresolve a taxonomic quagmire

MARKO MUTANEN,* MARI KEKKONEN,†‡ SEAN W. J. PROSSER,‡ PAUL D. N. HEBERT‡ and

LAURI KAILA†

*Biodiversity Unit, Department of Biology, University of Oulu, P.O. Box 3000, FI-90014 Oulu, Finland, †Zoology Unit, Finnish

Museum of Natural History, University of Helsinki, P.O. Box 17, FI-00014 Helsinki, Finland, ‡Biodiversity Institute of Ontario,

University of Guelph, Guelph, ON N1G 2W1, Canada

Abstract

Each holotype specimen provides the only objective link to a particular Linnean binomen. Sequence information

from them is increasingly valuable due to the growing usage of DNA barcodes in taxonomy. As type specimens are

often old, it may only be possible to recover fragmentary sequence information from them. We tested the efficacy of

short sequences from type specimens in the resolution of a challenging taxonomic puzzle: the Elachista dispunctella

complex which includes 64 described species with minuscule morphological differences. We applied a multistep pro-

cedure to resolve the taxonomy of this species complex. First, we sequenced a large number of newly collected speci-

mens and as many holotypes as possible. Second, we used all >400 bp examine species boundaries. We employed

three unsupervised methods (BIN, ABGD, GMYC) with specified criteria on how to handle discordant results and

examined diagnostic bases from each delineated putative species (operational taxonomic units, OTUs). Third, we

evaluated the morphological characters of each OTU. Finally, we associated short barcodes from types with the delin-

eated OTUs. In this step, we employed various supervised methods, including distance-based, tree-based and charac-

ter-based. We recovered 658 bp barcode sequences from 194 of 215 fresh specimens and recovered an average of

141 bp from 33 of 42 holotypes. We observed strong congruence among all methods and good correspondence with

morphology. We demonstrate potential pitfalls with tree-, distance- and character-based approaches when associating

sequences of varied length. Our results suggest that sequences as short as 56 bp can often provide valuable taxo-

nomic information. The results support significant taxonomic oversplitting of species in the Elachista dispunctella

complex.

Keywords: Automatic Barcode Gap Discovery, Barcode Index Number, Elachista, GMYC, species delimitation, species

delineation

Received 3 February 2014; revision received 4 December 2014; accepted 8 December 2014

Introduction

Since its introduction 12 years ago (Hebert et al. 2003a,b),

DNA barcoding has been widely applied by taxonomists

as indicated by hundreds of published taxonomic studies

utilizing DNA barcodes (Teletchea 2010). This work has

shown that DNA barcoding is particularly useful in its

main function, that is rapid identification of specimens.

Indeed, many studies have shown that DNA barcodes

allow the unambiguous identification of more than 90%

of species and typically place the remainder to a very

small number of closely related species (Barrett & Hebert

2005; Kerr et al. 2009; Lukhtanov et al. 2009; Dinca et al.

2011; Hausmann et al. 2011, 2013).

Besides specimen identification, DNA barcodes help

to support several other taxonomic tasks. An increasing

number of species owe their initial discovery to the

observation of barcode divergences among specimens

thought to represent a single species (e.g. Hebert et al.

2004; Segerer et al. 2010; Wilson et al. 2010; Huemer &

Mutanen 2012; Mutanen et al. 2012a,b, 2013; Yang et al.

2012; Landry & Hebert 2013). In addition, barcodes have

been used for delineating specimens into putative spe-

cies (e.g. Puillandre et al. 2012b; Kekkonen & Hebert

2014). The benefits of using DNA barcodes for species

delineation are clear especially when employed with

novel species delineation methods (e.g. General Mixed

Yule-coalescent GMYC (Pons et al. 2006; Monaghan et al.

2009; Fujisawa & Barraclough 2013). As delineation

methods generate a clearly defined result, the outcome isCorrespondence: Marko Mutanen, Fax: +358-8-5541061;

E-mail: marko.mutanen@oulu.fi

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.This is an open access article under the terms of the Creative Commons Attribution License, which permits use,distribution and reproduction in any medium, provided the original work is properly cited.

Molecular Ecology Resources (2015) 15, 967–984 doi: 10.1111/1755-0998.12361

repeatable, largely objective and results are easily com-

parable between studies.

In addition to species identification and delineation,

taxonomic work includes another crucially important

task: naming. After the discovery of a new species, a for-

mal taxonomic description is needed. This requires an

evaluation of the status of prior names. Primary type

specimens are central to this process as each taxonomic

binomen is attached to them. As there may be many

alleged synonyms, each with their own primary type

specimen(s), it is often not straightforward to rule out

the possibility that a putative new species already has a

valid name (i.e. is it truly new). Therefore, the examina-

tion of type specimen(s) is necessary to check the possi-

ble existence of name(s) available for each newly

discovered species. Particular difficulty in reaching a

decision often arises when type specimens are in poor

condition or when species show only small or uncertain

morphological divergence. DNA barcodes provide a use-

ful source of additional information for associating types

with newly delineated species (Puillandre et al. 2011).

Because type specimens tend to be old, only partial

sequences are usually recovered, but even short seg-

ments of the barcode region often provide enough infor-

mation to associate types to other specimens (Hajibabaei

et al. 2006; Meusnier et al. 2008; Shokralla et al. 2011).

However, short sequences can create complications for

efforts to link types with delineated species.

Because the identification of specimens, the delinea-

tion of species and the naming of newly discovered spe-

cies are different tasks, they require different tools and

procedures (see also Collins & Cruickshank 2013). Meth-

ods suitable for identification are supervised because

identifications must be based on an existing reference

database, while species delineation has a more explor-

atory nature, meaning that unsupervised approaches are

employed. When type barcodes are full-length, their

association with delineated species is straightforward. It

can be conducted simultaneously to the delineation of

species using only unsupervised methods (i.e. type bar-

codes are grouped in the same way as other sequences).

However, as noted above, barcode sequences recovered

from type specimens tend to be short due to DNA degra-

dation, and these short sequences considerably compli-

cate the association procedure because species

delineation methods are very sensitive to variation in

sequence length (see Material and methods). As a result,

the inclusion of short barcodes requires a two-stage pro-

cedure where species delineation is separated from the

association of types with species.

Both supervised and unsupervised methods can be

divided into three categories: distance- (or similarity)-,

tree- and character- (or diagnostic)-based. Methods rely-

ing on genetic distances [e.g. BLAST (Altschul et al. 1997),

BIN (Ratnasingham & Hebert 2013), ABGD (Puillandre

et al. 2012a)] and trees (usually gene trees) (e.g. neigh-

bour-joining (Saitou & Nei 1987), GMYC (Pons et al.

2006; Monaghan et al. 2009; Fujisawa & Barraclough

2013)] are most often applied, but some character-based

tools are also available [e.g. CAOS (Sarkar et al. 2008), BLOG

(Bertolazzi et al. 2009; Van Velzen et al. 2012; Weitschek

et al. 2013)]. Generally, these methods deliver rather con-

gruent results (Ratnasingham & Hebert 2013; Kekkonen

2014), but supervised character-based methods per-

form especially well with recently diverged species

(Van Velzen et al. 2012). However, as all methods have

drawbacks, it is reasonable to employ several methods

(Carstens et al. 2013).

Neither the use of DNA barcodes nor other single-

locus approaches are without problems (Dupuis et al.

2012). There are several biological processes that may

result in a deep intraspecific mitochondrial DNA split

(Hurst & Jiggins 2005; Rubinoff et al. 2006). Such splits,

with no further evidence of several species, have been

reported in several groups (Hausmann et al. 2011; Kvie

et al. 2012; Kodandaramaiah et al. 2013). Among the most

important mechanisms by which mtDNA may produce a

misleading taxonomic signal are historical polymor-

phism, hybridization followed by mitochondrial intro-

gression, prolonged isolation of populations without

speciation and selective sweeps of haplotypes induced

by Wolbachia (Funk & Omland 2003; Hurst & Jiggins

2005; Rubinoff et al. 2006). Due to these problems, taxo-

nomic revisions should not be based solely on DNA bar-

codes or any other single genetic marker, but sequence

clusters likely to represent different species should be

evaluated with additional characters such as morphol-

ogy or nuclear loci.

Highlighting the complexity of species delimitation,

cases where poorly documented taxonomic work com-

bined with weakly justified splitting of species has

resulted in a taxonomic dead-end where no specimen

can be reliably identified are not rare (Mutanen 2005).

The European Elachista dispunctella–triseriatella complex

(Lepidoptera: Elachistidae), subsequently referred to as

the E. dispunctella complex, appears to represent one

such case. The members of this species group are largely

restricted to Europe. All species with known life history

are leaf miners in grasses (Poaceae). Species occupy xero-

thermic habitats at both low and high altitudes. Based on

published information, many taxa have a very restricted

distribution. This pattern, in combination with the stated

high number of species, makes this group potentially

important in the conservation of biodiversity, but an

accurate taxonomy would be necessary. The E. dispunc-

tella complex formerly comprised just six species. The

first three, dating back to the 1800s, were based on

records from England, Austria and Sardinia, while the

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

968 M. MUTANEN ET AL .

other three were described during the 1900s, the most

recent addition in 1974 from Italy (see Appendix S1, Sup-

porting information). The E. dispunctella complex was

subsequently intensively studied by Traugott-Olsen

(1985, 1988, 1992) who partitioned it into 64 species. This

rise in the species count reflected a somewhat typological

concept of species with no intraspecific variation in char-

acters assumed. Rating the importance of characters

without explicit justification, considering slight differ-

ences in wing venation as the primary basis for sorting

the specimens into ‘sections of species’ led to a situation

where otherwise identical-looking specimens were

placed into distinct sections, and subsequently, other

characters were used to differentiate the species within

each section, with no comparison among species in dif-

ferent sections. This procedure, presuming no intraspe-

cific variation, led to the situation where over a third of

the newly described species (27 in all) were based on a

single specimen. For example, 11 species were reported

from one hillside in Austria, all but one described as new

by Traugott-Olsen, while six other new species were

found at a single site in the Sierra Nevada in Spain. The

species from these two localities show no morphological

differences apart from alleged minor differences in their

wing venation. The use of this character is problematic

because a study focused on Elachista showed that wing

venation characters such as those used by Traugott-Ol-

sen (1988, 1992) display intraspecific and even intra-indi-

vidual (due to asymmetry) variation far exceeding the

divergence used for the recognition of the sections of

species by Traugott-Olsen (1992) (Albrecht & Kaila 1997).

The species in the venation-based sections were based on

apparent differences in genitalic morphology following

the examination of a single or at best a few specimens,

but many of the differences employed for species diag-

nosis by Traugott-Olsen (1988, 1992) in Elachista appear

to reflect distortions in shape due to poor dissection or

involve differences in structures known to vary within

species in related groups (Kaila 1999, 2009; Kaila et al.

2001). Because type specimens are often damaged or

their shapes are distorted by poor dissections, subse-

quent efforts to resolve their status have failed. As the

current taxonomy of the E. dispunctella complex still

relies on characters of dubious or rejected value, so no

specimen can be identified with certainty (e.g. Kaila

2007, 2011a,b, 2012).

Due to this situation, the E. dispunctella complex is in

great need of taxonomic revision. However, many char-

acteristics of Elachista make this work very challenging.

These moths are tiny, possess few or no taxonomically

useful external characters, and their genitalia structures

are often difficult to interpret. As a consequence, a com-

prehensive taxonomic work would require a full-time

effort of many years. However, such slow process

responds poorly to the needs created by the accelerating

biodiversity loss, because obscurely defined species are

difficult to protect. This study aims to resolve this

dilemma through an integrated molecular and morpho-

logical approach (Fig. 1). The use of data from different

sources is well accepted as the best practice for extremely

challenging groups such as Elachista (Dayrat 2005). As a

first step, we sequenced many newly collected specimens

and as many holotypes as possible. Second, we used the

>400 bp DNA barcodes to re-examine species boundaries

employing three unsupervised methods (BIN, ABGD,

GMYC) with specified criteria how to handle discordant

results, and examined diagnostic bases from each delin-

eated putative species (here called operational taxonomic

units, OTUs). Third, we studied the morphological char-

acters of each OTU to avoid the limitations of single-

locus mtDNA. Finally, we associated short DNA bar-

codes from types with the delineated OTUs. In this step,

we employed various supervised methods, representing

all three categories (distance-based: pairwise distances,

Focal taxa

Re-examination of species boundaries based on DNA

barcodesBIN ABGD GMYC

diagnostic characters

FULL MATCH

PARTIAL MATCH DISCORDANT

Sympatry criterion

OTUs

Evaluation of OTUswith morphology

Associating types with OTUs using DNA barcodes

pairwise distances BOLD ID engineML BI NJ BLOG

Unsupervised methods Supervised methods

Congruent results

Conflicting/ ambiguous

results

Evaluation withmorphology

Descriptive part:Taxonomical revision

based on all data available and

possibly enhanced sampling

Fig. 1 Schematic presentation of the study. The molecular data

were divided into two subsets according to sequence length.

Only barcode sequences >400 bp were employed for OTU delin-

eation for re-examination of species boundaries (unsupervised,

exploratory methods), whereas both subsets were used for type

association (supervised, reference-based methods). Both molec-

ular stages (grey boxes) were followed by evaluation based on

morphological characters (male genitalia) to aid the elimination

of possible errors caused by single-locus DNA data. OTUs sup-

ported by both morphology and DNA will be preferred in the

taxonomic revision unless evidence supporting conflicting

boundaries (i.e. cryptic species) emerges during revisionary

work.

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

SHORT BARCODES FROM TYPES CLARIFY SPECIES STATUS 969

and BOLD Identification System; tree-based: neighbour-

joining, maximum likelihood and Bayesian inference;

character-based: BLOG).

Material and methods

Material acquisition and specimen sampling

Samples were obtained from the Finnish Museum of

Natural History (University of Helsinki, Finland),

National Museum of Natural History (Madrid, Spain),

Naturhistorisches Museum (Wien, Austria), Netherlands

Centre for Biodiversity Naturalis (Leiden, the Nether-

lands), The Natural History Museum (London, UK),

Tiroler Landesmuseum Ferdinandeum (Innsbruck, Aus-

tria), Zoological Museum, Natural History Museum

(Copenhagen, Denmark) and the private collections of

Peter Buchner, Per Falck, Bob Heckford, Jari Junnilainen,

Jari Kaitila, Kari and Timo Nupponen, Jukka Tabell,

Zdenko Tok�ar and Christian Wieser.

Altogether 215 nontype specimens and 42 holotypes

were sampled and sequenced. A single leg (usually a

hind leg) was used for DNA extraction. Abdomens could

not be utilized for DNA extraction because they had

been permanently mounted following genital dissection.

The nontype specimens were relatively fresh, the oldest

collected in 1984, and 83% collected since 2000. We

sequenced specimens from across the distribution of the

Elachista dispunctella complex, directing particular effort

to material collected at or near the type localities of the

described species.

We extracted DNA from 42 of the 64 holo- and lecto-

types in the E. dispunctella complex (Table S2, Supporting

information). One paratype (E. svenssoni) was also

included, but it did not yield sequence data. With the

exception of three species (E. mannella, E. pocopunctella

and E. svenssoni) for which the year of collection of holo-

types is unknown, all holotypes were collected during

the 1900s, the youngest in 1989 and the oldest in 1936.

The average age of the type specimens was 33 years

(Fig. 2).

Collection data, Barcode Index Numbers (BINs) and

GenBank Accession nos for the specimens are provided

in Table S3 (Supporting information). All specimen and

sequence data are available in the public dataset DS-EL-

ADIS in BOLD (www.boldsystems.org) (dataset DOI:

http://dx.doi.org/10.5883/DS-ELADIS).

Sequencing protocol for nontype specimens

All DNA sequencing was conducted at the Canadian

Centre for DNA Barcoding (CCDB). Sequencing of non-

type specimens followed routine DNA barcoding proto-

cols described by Ivanova et al. (2006) and deWaard et al.

(2008). In short, this involves silica membrane-based

DNA extraction followed by standard PCR using the

primers listed on the sequence page for each record on

BOLD although the LepF1-LepR1 primer set was always

used in the first trial. PCR was performed in a total reac-

tion volume of 12.5 lL, and PCR products were assayed

using the Invitrogen E-gel 96 system. In cases where

amplification was successful, 2 lL of the PCR product

was sequenced. Sequencing followed protocols described

in Hajibabaei et al. (2006) with each reaction performed

in a final volume of 10.5 lL. Sequencing reactions were

purified using either Sephadex columns or magnetic

beads and sequenced on an ABI 3730 XL capillary

sequencer.

Sequencing protocol for type specimens

Due to the age of the type specimens (all >24 years), all

molecular work with the exception of DNA sequencing

was performed in a clean laboratory dedicated to the

analysis of old specimens at the CCDB and employed

sterile reagents and equipment. DNA extraction was per-

formed in the same manner as nontype specimens except

that the DNA was eluted from the silica membranes

using 30 lL of molecular-grade water vs. the standard

elution volume of 40 lL. PCR was performed using vari-

ous primers that target ~100- to 400 bp fragments of the

COI barcode region. In cases where multiple overlapping

fragments were recovered, they were combined to form

a longer contiguous sequence. PCR cycling conditions

were similar to those used for nontype specimens, but

due to the low quantity of template DNA, 20 additional

cycles were performed to amplify the target fragment to

the concentration required for Sanger sequencing. Due to

the increased risk of contamination, PCR products from

type specimens were not visualized on E-gels, but were

directly sequenced using standard protocols. The result-

ing sequences were edited using CODONCODE v. 3.0.1

0

100

200

300

400

500

600

700

1930 1940 1950 1960 1970 1980 1990 2000

Sequ

ence

leng

th (b

p)

Collection year

Fig. 2 Length of DNA barcode sequences from 42 type speci-

mens in the Elachista dispunctella complex.

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

970 M. MUTANEN ET AL .

(CodonCode Corporation, Dedham, MA, USA). In addi-

tion, amino acids in sequences from type specimens were

studied for nonsynonymous substitutions that are rare

among closely related species and thus might reveal con-

tamination events that would otherwise be overlooked.

Sequence-based OTU delineation

All sequences were aligned using the Aligner in BOLD

v3.6 (Ratnasingham & Hebert 2007) and then inspected

visually for stop codons and frameshift mutations in

MEGA 5.0 (Tamura et al. 2011). DNA barcode sequences

longer than 400 bp were used for OTU delineation with

three methods: RESL algorithm which forms the basis of

Barcode Index Number system (BIN; Ratnasingham &

Hebert 2013), Automatic Barcode Gap Discovery

(ABGD; Puillandre et al. 2012a) and General Mixed

Yule-coalescent (GMYC; Pons et al. 2006; Monaghan et al.

2009; Fujisawa & Barraclough 2013). The first two meth-

ods form OTUs based on different clustering algorithms

and employ genetic distances, whereas GMYC is a

model-based likelihood method which seeks to deter-

mine the threshold between speciation and coalescent

events from an ultrametric gene tree. The lineages cross-

ing the threshold line represent the OTUs delineated by

GMYC. No character-based methods were used due to

the lack of a feasible tool, but diagnostic base substitu-

tions between delineated OTUs were examined.

Normally, sequences are automatically assigned to a

BIN on the BOLD WORKBENCH v3.6, but these BIN assign-

ments are based on the analysis of all barcode sequences

on BOLD (currently 3.6 M). These BIN assignments are

not strictly comparable with the other two delineation

methods whose outcomes are based solely on analysis of

the sequence collected in this study. To avoid this prob-

lem, we performed a RESL ‘stand-alone’ analysis, using

only the dataset in this publication (DS-ELADIS). This

version is not currently available for public use, but it

will be released in the near future. ABGD analyses were

performed at the web interface (http://wwwabi.snv.jus-

sieu.fr/public/abgd/, web version April 11 2013) on 30

January 2014; for source code, see Appendix S4 (Support-

ing information) (downloaded on 30 January 2014) using

a default value of relative gap width (X = 1.5) and all

available distance metrics [p-distance, JC69 (Jukes &

Cantor 1969), K2P]. All other parameter values employed

defaults. The General Mixed Yule-coalescent (GMYC)

method requires a fully-resolved ultrametric gene tree as

input for the analysis. We constructed a Bayesian infer-

ence tree in BEAST (Drummond et al. 2006; Drummond &

Rambaut 2007) employing a coalescent tree prior (con-

stant size; Kingman 1982). The coalescent prior was cho-

sen due to the structure of the dataset (relatively small

number of closely related species and generally high

intraspecific sampling). However, previous results have

indicated that tree prior has only minor effect on GMYC

results (Kekkonen 2014). XML files were made with

BEAUTI v1.7.1 interface with the following settings:

GTR+G+I substitution model, empirical base frequencies,

four gamma categories, all codon positions partitioned

with unlinked base frequencies and substitution rates.

An uncorrelated relaxed lognormal clock model was

used with rate estimated from the data and ucld.mean

parameter with uniform prior with values 0 as a lower

boundary and 10 as an upper boundary. All other set-

tings were left as defaults. The applied XML file is avail-

able as Appendix S5 (Supporting information). The

length of the MCMC chain was 40 000 000 sampling

every 4000. BEAST runs were executed in Bioportal

(Kumar et al. 2009) and CIPRES (Miller et al. 2010). The

ESS values and trace files of runs were evaluated in

TRACER v1.5.0. Two independent runs were merged using

LOGCOMBINER v1.7.1 with 20% burn-in. A maximum clade

credibility tree with a 0.5 posterior probability limit and

node heights of target tree was constructed in TREEANNO-

TATOR v1.7.1. Both single and multiple threshold model

analyses were conducted in R (R Core Team 2012) using

the APE (Paradis et al. 2004) and SPLITS (Ezard et al. 2009)

packages (for R code used for GMYC analyses, see

Appendix S6, Supporting information). GMYC analyses

were performed with haplotype data collapsed in ALTER

(Glez-Pe~na et al. 2010). ALTER is a web interface (http://

sing.ei.uvigo.es/ALTER/), which was used to extract

one representative of each haplotype (i.e. it discards all

but one copy of each sequence in the dataset). GMYC

should always be used with haplotype data as the inclu-

sion of identical sequences can generate erroneous

results (Monaghan et al. 2009).

The results of BIN, ABGD and GMYC were com-

pared, and all OTUs were divided into three categories

(FULL MATCH where all methods provide identical

results, PARTIAL MATCH where two of three methods

delineated the same OTU, DISCORDANT where all

three methods show conflicting results) following the

procedure introduced by Kekkonen & Hebert (2014). The

PARTIAL MATCH and DISCORDANT categories

involve cases where the results of the three delineation

methods are in conflict, invoking a question as to which

OTUs should be considered as valid and which

should be rejected. According to the sympatry criterion

(see Fig. 4, in Kekkonen & Hebert 2014), sympatric OTUs

should be accepted while those in allopatry should be

rejected. Although not a definitive argument, a sympatric

split suggests the presence of separate species, while an

allopatric split may simply reflect geographic variation

in a single taxon.

Diagnostic bases of each OTU were studied on the

BOLD WORKBENCH v3.6, employing the ‘Diagnostic Char-

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

SHORT BARCODES FROM TYPES CLARIFY SPECIES STATUS 971

acters’ tool. Although this tool does not ordinarily allow

the inclusion of singletons and doubletons, an exception

was made with support from BOLD staff to allow the

analysis of all OTUs. All codon positions were analysed.

Due to the inclusion of singletons and doubletons, we

recorded only pure diagnostic characters (sensu Sarkar

et al. 2008).

OTU evaluation with morphology

Due to the limitations of single-locus mtDNA as the sole

basis for taxonomic decisions, all OTUs delineated in

the first phase were validated by examining the mor-

phology of male genitalia (Fig. S7, Supporting informa-

tion). We chose morphology instead of the examination

of sequence diversity in one or more nuclear loci,

because recovery of such data from small, often old

specimens is difficult. The morphology of representative

specimens from each OTU was examined using charac-

ters that have repeatedly been shown to match species

boundaries in better known species groups of Elachista

(see Mutanen et al. 2013 and references therein). The

chief morphological differentiation in the E. dispunctella

complex involves genitalia. The characters used were

those detailed by Traugott-Olsen & Nielsen (1977) and

Kaila (2011c). Females could not be systematically stud-

ied, as they are unknown for several of the OTUs. In

OTUs where both sexes were available, the possibility of

using variation in female genitalia (Fig. S8, Supporting

information) for their diagnosis was examined. The

exploration of potential differences within and between

OTUs was based on measurements of the relative sizes

and shapes of genital structures. In addition to OTU val-

idation, the male genitalia of all holotypes were also

studied to enable comparison with conclusions based on

DNA analysis.

Association of type barcodes with OTUs

The inclusion of short sequences considerably altered the

composition of OTUs in both the ABGD and GMYC

analyses, indicating their inappropriateness for associat-

ing the short sequences from types with the longer

sequences obtained from recently collected specimens.

The RESL algorithm was not tested, but it likely suffers

from the same problem as ABGD because both are dis-

tance-based methods. As a consequence, we employed a

different set of methods to associate the short barcode

sequences from types with OTUs recognized in the OTU

delineation phase. We employed three tree-based

approaches: maximum likelihood, Bayesian inference

and neighbour-joining; two distance based: comparison

of pairwise distances and the matching algorithm of the

BOLD Identification System; and one character based:

BLOG (Bertolazzi et al. 2009; Van Velzen et al. 2012; Weit-

schek et al. 2013).

The neighbour-joining analysis was performed with

MEGA 5.0 using both K2P and uncorrected p-distance

models with pairwise deletion of missing data. The max-

imum-likelihood analysis was performed with RAXML

BLACKBOX (Stamatakis et al. 2008) using the GTR+Gmodel and default bootstrap settings. Bayesian analysis

was performed with the same data following the above-

mentioned protocol using BEAST, although no haplotype

collapsing was conducted (for XML file, see Appendix

S9, Supporting information).

Problems may arise if a short sequence shows no dif-

ference from two longer ones which differ from each

other by substitutions in regions outside the short

sequence region. Under such conditions, the assignment

of short sequences to a particular OTU can be mislead-

ing. To examine how neighbour-joining and maximum

likelihood behaved under these circumstances, we tested

an arbitrary dataset of five slightly modified sequences

from our data (nucleotides arbitrarily modified so that

the short test fragment was identical with two long

sequences of different OTUs) and observed that RAXML

(maximum likelihood) associated the short sequence

with just one of the identical sequences, while MEGA and

the ‘Taxon ID Tree’ tool in BOLD (neighbour-joining)

placed it as a distinct branch halfway between the two

full-length sequences that it matched. Both outcomes are

unsatisfactory because they are potentially misleading as

the real situation cannot necessarily be depicted in form

of a single fully-resolved tree. As we were not aware if

any of the short sequences in our dataset were identical

with long sequences of two or more species, we calcu-

lated pairwise K2P distances between each type and

every one of the longer sequences to allow distance-

based assignments. Regarding treatment of missing data,

the ‘pairwise comparison’ option of MEGA was used.

Another distance-based method used here, the BOLD

Identification System, is a part of BOLD Systems inter-

face. Each type barcode was searched against all barcode

records with the minimum length of 500 bp on the

BOLD database (analyses performed on January 2014).

As only one barcode from a type specimen was full-

length (E. moroccoensis) and all other were shorter than

500 bp, the query sequences were not used as reference

sequences (excluding E. moroccoensis) and thus matching

a sequence with itself was not possible.

BLOG analyses were performed with BLOG v2.0 offline

user interface. The method uses standard machine learn-

ing approach and requires two types of input: training

and test datasets (Weitschek et al. 2013). BLOG creates

logic classification formulas for each species (here OTUs)

based on diagnostic characters and subsequently

employs them to associate test sequences. We performed

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

972 M. MUTANEN ET AL .

four analyses using different sequence lengths [654 bp

(the original length), 162 bp, 93 bp, 54 bp]. The

sequences were trimmed in MEGA. The rationale behind

the length reduction was to maximize the number of

shared characters (i.e. bases) between types and non-

types. After trimming, each dataset was divided into two

parts. For training, we used all nontypes and E. morocco-

ensis, as in the OTU delineation phase, with all sequences

named after their corresponding OTU. The test datasets

included all barcodes from type specimens. The species

names of types were changed to OTU names following

the results of other type association methods (see Fig. 3).

The change of names was made due to the requirements

of BLOG as the names must match between the training

and test datasets. Hence, BLOG was employed to test the

results of other analyses. All parameters employed

defaults.

Results

Sequencing success

Of the 215 nontype specimens, 194 (90.3%) yielded

sequences and most (177) sequences were >600 bp.

Sequencing success depended on specimen age, even

though 16 of 21 unsuccessful specimens were collected

after 2000 (logistic regression: estimate = �3.07, n = 208,

P = 0.002). Failures in sequence recovery are unlikely to

result from primer mismatching because those

employed are effective across all lepidopteran families.

Instead, sequencing failure likely reflects poor DNA

preservation in some specimens. Sequencing success is

generally known to decrease as a factor of specimen

age (Meusnier et al. 2008), but exposure to certain

chemicals leads to DNA degradation and the past pres-

ervation conditions of some specimens were not

known.

About 75% (33 of 42) of the holotypes yielded some

sequence information. There was no correlation between

sequencing success or sequence length and specimen age

(logistic regression: estimate = �1.82, n = 37, P = 0.069;

Spearman rank correlation: RS = �0.271, n = 37,

P = 0.104, respectively). These sequences averaged

141 bp in length (Fig. 2), but a 658 bp barcode was recov-

ered from the youngest type specimen, E. moroccoensis

(collected 1989). However, 17 type specimens generated

sequences <100 bp in length and only 56 bp were recov-

ered from six specimens.

OTU1

OTU2OTU3

0.004

GM

YCD

GB

ANI

B TypesE.oukaimedenensis*

E.gerdmaritella*

E. vanderwolfi

E. berndtiella

E. glaseriE. olemartiniE. senecaiE. rikkeaeE. wadielhiraensisE. bengtssoniE. rissaniensis

E. multipunctellaE. karsholtiE. povolnyiE. intrigellaE. nilspederiE. skulei*E. imbi

E. moroccoensisE. anitella

E. blancella

E. baldizzonellaE. veletaellaE. toveella

E. varensisE. hispanicaE. occidentella

None

NoneNone

E. minusculella

OTU4OTU5OTU6

OTU7

OTU8OTU9OTU10OTU11

OTU12

OTU13

OTU14OTU15

OTU16

NoneOTU17OTU18

None

None

E. tribertiella*E. bazaella*E. louiseae*

None

NoneNone

100/1

84/1

100/1

100/1

100/1

98/1

100/1

98/1

100/1

99/1

100/1

100/1

100/1

100/-

94/1

E. michelseni

E. casascoensis#

ohproM

Fig. 3 A Bayesian inference tree used for

OTU delineation via GMYC (includes

>400-bp sequences) with bootstrap (from

RAxML analysis) and posterior probabil-

ity values (on the left). Coloured bars

indicate delineated OTUs by different

methods (Barcode Index Numbers BIN,

Automatic Barcode Gap Discovery

ABGD, General Mixed Yule-coalescent

GMYC and morphology) (in the middle).

‘Types’ includes all DNA barcodes of type

specimens associated with OTUs in this

study (on the right). Types marked with *have controversial results based on either

conflicting results between the used meth-

ods or rather long distance to the nearest

OTU (see text for further information).

E. casascoensis (marked with #) is placed

according to the distance measures,

BLOG results and morphology as none of

the tree-based methods associated E. cas-

ascoensis with any OTU.

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

SHORT BARCODES FROM TYPES CLARIFY SPECIES STATUS 973

OTU delineation

Altogether 191 DNA barcodes (>400 bp) were used for

OTU delineation, including the full-length sequence

from the holotype of E. moroccoensis (78 haplotypes; the

MSA used for OTU delineation is provided as a FASTA

file in Appendix S10, Supporting information). The BIN

analysis led to the recognition of 21 OTUs while ABGD

resulted in a stable OTU count (19) with a range of prior

intraspecific values (P = 0.0077–0.0215) with both JC69

and K2P initial and recursive partitions (Fig. S11, Sup-

porting information). As a result, this count (19) was

used in the later analyses (Table 1). The recursive parti-

tions also produced higher OTU counts (20-62 OTUs)

when the P-value was 0.0046, but we adopted the OTU

count of 19 due to its stability across a range of higher P-

values. Far more variable outcomes were produced

when p-distances were employed with OTU counts

ranging from 1 to 35 (initial partition) and from 1 to 82

(recursive partitions) (Table 1). Because of their strong

discordance from other results with ABGD and those

obtained with other methods, these results are not con-

sidered further. Both single and multiple threshold mod-

els of GMYC outperformed the null model, indicating

the presence of more than one species in the dataset

(Table 2). The result from the single threshold model (22

OTUs) was adopted as the multiple threshold model

(which resulted in 27 OTUs) did not improve fit to the

data (v2 = 2.24, d.f. = 6, P < 0.9).

The three methods produced congruent results with

three exceptions where OTUs were assigned to the PAR-

TIAL MATCH category (see the diagonal lines on the

BIN, ABGD and GMYC columns in Fig. 3). Both BIN and

GMYC partitioned members of OTU7 and OTU18,

whereas ABGD merged them into a single OTU. GMYC

also divided OTU4, while BIN and ABGD did not. Fol-

lowing the sympatry criterion of Kekkonen & Hebert

(2014), the split of OTU18 and OTU7 was ignored as it

involved allopatric clusters. However, the two specimens

of OTU4 were collected from the same place on the same

day and thus distance-dependent difference cannot

explain the recognized genetic variation. Because of their

sympatry, these two OTUs were kept separate at this

stage. Following this rationale, the total number of OTUs

was 19 among the nontype specimens. The total OTU

count was 20 because the DNA barcode from the type

specimen of E. moroccoensis did not cluster with any

recently collected specimen (i.e. the type specimen

formed its own OTU) (Fig. 3).

Diagnostic bases were designated based upon the

analysis of the same 191 barcode sequences which were

used for OTU delineation. Diagnostic characters were

discovered for all OTUs, with from 2 to 14 diagnostic

substitutions per OTU (Table 3).

Table 1 The number of OTUs recognized by Automatic Barcode Gap Discovery (ABGD) analyses among 191 COI sequences >400 bp

using three distance metrics

Subst. model X Partition

Prior intraspecific divergence (P)

0.0599 0.0359 0.0215 0.0129 0.00774 0.00464 0.00278 0.00167 0.001

P 1.5 Initial 1 5 35 35 35 35 35 35 35

Recursive 1 5 37 40 40 44 50 50 82

JC 1.5 Initial 0 19 19 19 19 19 19 19

Recursive 0 19 19 19 20 25 27 62

K2P 1.5 Initial 0 19 19 19 19 19 19 19

Recursive 0 19 19 19 20 25 27 62

X, relative gap width; P, p-distance; JC69, Jukes-Cantor; K2P, Kimura 2-parameter.

Table 2 Results of the General Mixed Yule-coalescent (GMYC) analyses for OTU formation (>400 bp sequences, n = 191, haplo-

types = 78)

Analysis Clusters (CI) Entities (CI) Likelihoodnull LikelihoodGMYC Likelihood ratio Threshold

Single 14 (13–15) 22 (20–25) 644.652 660.319 31.33367 (***) �0.001613839

Multiple 16 (15–16) 27 (24–28) 644.652 661.441 33.57823 (***) �0.001613839

�0.001016101

�0.0002361306

Clusters, OTUs delineated by GMYC with more than one specimen; Entities, all OTUs (clusters and singletons) delineated by GMYC; CI,

confidence interval; Likelihoodnull: likelihood of the null model; LikelihoodGMYC, likelihood of the GMYC model; Threshold, the thresh-

old between speciation and coalescence processes; Single, single threshold model; Multiple, multiple threshold model. *** P < 0.001.

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

974 M. MUTANEN ET AL .

Evaluation of OTUs with morphology

The OTUs delineated on the basis of sequence variation

in the DNA barcode region corresponded closely with

morphological insights with three exceptions. First, spec-

imens of the sister OTUs 9 and 10 appeared morphologi-

cally indistinguishable. Second, the holotype of

E. moroccoensis, which formed an OTU of its own, and

OTU18 appear indistinguishable by morphology. Third,

the specimens belonging to OTU4, which was divided

into two sympatric OTUs by GMYC showed no morpho-

logical differences. As the other OTUs were congruent

between DNA and morphology, the presence of 17 OTUs

was supported by this phase (Fig. 3). Table 4 provides a

key for these OTUs based on their morphology.

Associating type sequences with OTUs

Neighbour-joining (Fig. S12, Supporting information),

maximum likelihood (Fig. S13, Supporting information)

and Bayesian inference (Fig. S14, Supporting informa-

tion) analyses were performed with 223 sequences (the

MSA used for type association is provided as a FASTA

file in Appendix S15, Supporting information). Because

none of these tree-building methods is an actual species

delineation method, they do not produce defined clus-

ters so each type specimen was associated with the near-

est OTU based on monophyly or sister group

relationship. We considered sister lineages conspecific if

their sequence divergence was less than 1%. All OTUs

were monophyletic in all trees. In general, the three

Table 3 Diagnostic characters of delineated operational taxo-

nomic units (OTUs)

OTU Diagnostic characters

Elachista OTU1 11

Elachista OTU2 6

Elachista OTU3 7

Elachista OTU4 14

Elachista OTU5 7

Elachista OTU6 5

Elachista OTU7 7

Elachista OTU8 6

Elachista OTU9 4

Elachista OTU10 2

Elachista OTU11 2

Elachista OTU12 11

Elachista OTU13 9

Elachista OTU14 4

Elachista OTU15 4

Elachista OTU16 6

Elachista OTU17 4

Elachista OTU18 2

Elachista OTU19 3

Table 4 Morphological differentiation between operational tax-

onomic unit’s (OTU’s) in the Elachista dispunctella complex

expressed as an identification key. Some species appear more

than once in the key, as the characters sometimes display intra-

specific variation, or variation in dissection technique and suc-

cess may yield apparent although not real differences. For some

species, the diagnostic morphological characters are found in

one sex only

1. Forewing fringe scales distally grey forming grey distal line

along termen__ 2

– Forewing fringe scales white, sometimes with single dark grey

or brown tips of otherwise white scales __3

2. Juxta lobes with at least 5 distinctive setae; female bursa

oval__ OTU 14

– Juxta lobes without or with at most two small setae; female

bursa divided into two portions separated by median

narrowing__OTU 13

3. Digitate process twice as long as juxta lobes__ OTU 18; E.

moroccoensis

– Digitate process at most as long as juxta lobes__4

4. Phallus longer than valva__ OTU 15

– Phallus at most as long as valva__5

5. Uncus lobes narrow, three times as long as wide__OTU 5

– Uncus lobes at most twice as long as broad__6

6. Phallus with curved apex__7

– Phallus with straight apex__11

7. Forewing unicolorous white; digitate process laterally

orientated__OTU 4

Forewing with scattered dark grey scales; digitate process

posteriorly orientated__8

8. Length of phallus 5/6 of valva; juxta lobes as long as digitate

process__OTU 7

– Length of phallus at most 2/3 of valva__9

9. Digitate process elongate, at least three times as long as

wide__OTU 2

– Digitate process broad and triangular, length at most twice its

width at base__10

10. Juxta lobes reduced__OTU 1

– Juxta lobes developed, as large as digitate process__OTU 3

11. Juxta lobes longer than uncus lobes__OTU 7

– Juxta lobes shorter than uncus lobes__12

12. Uncus lobes laterally produced, elongate, with pointed

apex__OTU 6

– Uncus lobes posteriorly directed, with rounded or at most

slightly lateroposteriorly conical apex__13

13. Phallus as long as valva__14

– Phallus shorter than valva__15

14. Female ostium bursae as wide as deep__ OTU 8

– Female ostium bursae three times as wide as deep__ OTU 9;

OTU 10

15. Valva somewhat S-shaped, narrowest medially; phallus

basally significantly broader than distally__OTU 12

– Valva straight, parallel-sided; phallus slender, hardly tapered

toward apex__16

16. Uncus lobes as long as broad__OTU 17

– Uncus lobes longer than broad__17

17. Valva 3 x as long as its width basally__OTU 16

– Valva 4 x as long as its width basally__18

18. Valva 4 x as long as digitate process__OTU 11

– Valva 5 x as long as digitate process__OTU 9; OTU 10

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

SHORT BARCODES FROM TYPES CLARIFY SPECIES STATUS 975

methods produced concordant associations of each type

with a single OTU (Fig. 3, Table 5). Only one type,

E. casascoensis, was inconsistently associated. It was

placed as a sister lineage to OTUs 8 and 9 (NJ) or as a sis-

ter lineage to OTUs 9 and 10 (ML, BI). Aside from this

discordance, there were two other cases with slight insta-

bility in type association with an OTU (E. oukaimedenensis

and E. gerdmaritella). In both instances, the type barcode

was nested within one OTU (E. oukaimedenensis with

OTU1 and E. gerdmaritella with OTU13) in the maxi-

mum-likelihood tree. However, Bayesian inference and

neighbour-joining analyses placed E. oukaimedenensis as

a sister lineage to OTU1. E. gerdmaritella was included

within OTU13 in the Bayesian tree, but was placed as a

sister lineage to OTU16 in neighbour-joining analysis.

E. skulei was placed as a sister lineage to OTU8, and

E. tribertiella, E. bazaella and E. louiseae came out as a sis-

ter lineage of OTU10 in all trees.

Comparison of pairwise distances largely supported

results from the tree-based methods, especially maxi-

mum likelihood and Bayesian inference with two excep-

tions (Table 6). First, E. vanderwolfi showed no

divergence from OTU12 or OTU15. Second, E. casascoen-

sis was associated with OTU7, but it remained distinct in

all tree-based results. Closer analysis indicated that the

first case was an artefact caused by the limited overlap

between type sequences and incomplete sequences from

some recent specimens. All other sequences produced a

Table 5 The type specimens associated with OTUs according to tree-based methods (maximum likelihood, Bayesian inference, neigh-

bour-joining with K2P) with bootstrap and posterior probability values. Discovered differences in amino acids between types and corre-

sponding OTUs are also given. E. casascoensis and E. moroccoensis are excluded as they associated with none of the OTUs in tree-based

analyses

OTU TypesML BootstrapML TypesBI PP TypesNJ BootstrapNJ Amino acids

1 E. oukaimedenensis 88 E. oukaimedenensis* 0.97 E. oukaimedenensis* 63

7 E. berndtiella 48 E. berndtiella 0.6 E. berndtiella 60

8 E. multipunctella 79 E. multipunctella 0.99 E. multipunctella 30

E. karsholti E. karsholti E. karsholti

E. povolnyi E. povolnyi E. povolnyi

E. intrigella E. intrigella E. intrigella

E. imbi E. imbi E. imbi

E. nilspederi E. nilspederi E. nilspederi

E. skulei* E. skulei* E. skulei*

9 E. bazaella* 69 E. bazaella* 1 E. bazaella* 53

E. louiseae* E. louiseae* E. louiseae*

E. tribertiella* E. tribertiella* E. tribertiella*

10 E. baldizzonella 73 E. baldizzonella 0.99 E. baldizzonella 48

E. veletaella E. veletaella E. veletaella

E. toveella E. toveella E. toveella

12 E. glaseri 56 E. glaseri 0.97 E. glaseri 32

E. olemartini E. olemartini E. olemartini

E. senecai E. senecai E. senecai

E. rikkeae E. rikkeae E. rikkeae

E. wadielhiraensis E. wadielhiraensis E. wadielhiraensis

E. bengtssoni E. bengtssoni E. bengtssoni

E. rissaniensis E. rissaniensis E. rissaniensis

E. michelseni E. michelseni E. michelseni

13 E. gerdmaritella 84 E. gerdmaritella 0.98 NO One difference

15 E. vanderwolfi 36 E. vanderwolfi 0.96 E. vanderwolfi 19

E. varensis E. varensis E. varensis

E. hispanica E. hispanica E. hispanica

E. occidentella E. occidentella E. occidentella

16 NO NO E. gerdmaritella* 41 One difference

17 E. anitella 89 E. anitella 1 E. anitella 52

E. blancella 56 E. blancella 0.94 E. blancella 22 One difference

E. minusculella E. minusculella E. minusculella

OTU, operational taxonomic unit; TypesML, TypesBI, TypesNJ, types associated with OTUs in maximum likelihood, Bayesian inference

or neighbour-joining (respectively) analyses; BootstrapML, BootstrapNJ, boostrap values of OTUs including types in maximum likelihood

or neighbour-joining (respectively) analyses; PP, posterior probability values of OTUs including types in analysis.

*Type specimen associated as a sister to its corresponding OTU.

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

976 M. MUTANEN ET AL .

result identical with the tree-based type association. Bar-

code sequences from type specimens were also searched

against records on BOLD. Based on the results of the

BOLD Identification System, 25 of 31 types were

matched with specimens on the database with more than

97.83% similarity (Table 7). The other six barcodes from

type specimens were too short (56 bp) to gain a result.

The outcome supported the result of pairwise distances

as E. casascoensis was associated with OTU7. Otherwise,

the results were congruent with other methods.

In the results of the BLOG analyses, the shortest

sequence length (54 bp) produced the highest number of

associated types as all but two were correctly linked with

an OTU (i.e. they supported the results of the other asso-

ciation methods) (Table 8). The two discordant results

included the type of E. skulei, which was associated with

OTU7 instead of OTU8, and E. minusculella, which was

not associated with any OTU. By contrast, the BLOG analy-

sis with full-length sequences (654 bp) resulted in only

three types being correctly associated with OTU9. All the

rest were unlinked to any OTU. The full-length barcode

of E. moroccoensis was only associated with itself. The

logic formulas of BLOG included one to four distinctive

characters for each OTU and changed with the sequence

length (Appendix S16, Supporting information).

In general, the 658 bp sequence from the type of

E. moroccoensis formed a unique OTU in all analyses,

supporting its distinctiveness from the other specimens

(Fig. 3). The type of E. casascoensis did not cluster with

any OTU in the tree-based analyses, but both distance

Table 6 Best correspondence (by least K2P distance) between the short sequences from 33 type specimens in the Elachista dispunctella

complex and operational taxonomic units (OTUs). The second column indicates the length of the sequence in base pairs. The values in

the columns 3–5 are minimum K2P distances between the type specimens and OTUs in question

Type

Seq.

length Best hit 2nd best hit 3rd best hit

E. anitella 164 OTU17 (0.000) OTU18 (0.012) OTU7 (0.031)

E. baldizzonella 164 OTU10 (0.000) OTU9 (0.019) OTU7 (0.019)

E. bazaella 94 OTU9 (0.011) OTU7 (0.033) OTU8, OTU10, OTU17, OTU18 (0.044)

E. bengtssoni 325 OTU12 (0.000) OTU16 (0.038) OTU1 (0.042)

E. berndtiella 164 OTU7 (0.000) OTU9, OTU10, OTU13,

OTU15 (0.025)

OTU18 (0.031)

E. blancella 164 OTU18 (0.006) OTU17 (0.019) OTU7 (0.038)

E. casascoensis 164 OTU7 (0.006) OTU10 (0.012) OTU9 (0.012)

E. gerdmaritella 94 OTU13 (0.011) OTU16 (0.022) OTU7 (0.044)

E. glaseri 94 OTU12 (0.000) OTU15 (0.022) OTU7 (0.027)

E. hispanica 94 OTU15 (0.000) OTU12 (0.013) OTU7 (0.022)

E. imbi 164 OTU8 (0.000) OTU9 (0.025) OTU10 (0.031)

E. intrigella 164 OTU8 (0.000) OTU9 (0.025) OTU10 (0.031)

E. karsholti 94 OTU8 (0.000) OTU9 (0.033) OTU5, OTU7, OTU10 (0.044)

E. louiseae 56 OTU9 (0.018) OTU7 (0.037) OTU6, OTU8, OTU10, OTU17, OTU18 (0.056)

E. michelseni 56 OTU12 (0.000) OTU15 (0.018) OTU1, OTU16 (0.037)

E. minusculella 56 OTU18 (0.000) OTU17 (0.018) OTU5 (0.037)

E. moroccoensis 658 N/A OTU18 (0.045) OTU17 (0.051)

E. multipunctella 164 OTU8 (0.006) OTU9 (0.031) OTU10 (0.038)

E. nielspederi 164 OTU8 (0.000) OTU9 (0.025) OTU10 (0.031)

E. occidentella 164 OTU15 (0.000) OTU7 (0.025) OTU12 (0.028)

E. olemartini 94 OTU12 (0.000) OTU15 (0.022) OTU7 (0.027)

E. oukaimedenensis 94 OTU1 (0.014) OTU7 (0.044) OTU12, OTU15, OTU17 (0.055)

E. povolnyi 56 OTU8 (0.000) OTU5, OTU7, OTU9,

OTU10 (0.037)

OTU18 (0.057)

E. rikkeae 94 OTU12 (0.000) OTU15 (0.022) OTU7 (0.027)

E. rissaniensis 164 OTU12 (0.000) OTU7 (0.031) OTU10, OTU13, OTU15, OTU16 (0.038)

E. senecai 93 OTU12 (0.000) OTU15 (0.022) OTU7 (0.027)

E. skulei 164 OTU8 (0.019) OTU7 (0.031) OTU9, OTU10, OTU15, OTU17, OTU18 (0.038)

E. toveella 164 OTU10 (0.000) OTU7, OTU9 (0.019) OTU8, OTU12 (0.031)

E. tribertiella 90 OTU9 (0.011) OTU7 (0.034) OTU8, OTU10, OTU17, OTU18 (0.046)

E. wadielhiraensis 164 OTU12 (0.000) OTU7 (0.025) OTU10, OTU13, OTU15, OTU16 (0.031)

E. vanderwolfi 56 OTU12, OTU15 (0.000) OTU1 (0.018) OTU5 (0.037)

E. varensis 94 OTU15 (0.000) OTU12 (0.013) OTU7 (0.022)

E. veletaella 56 OTU10 (0.000) OTU7 (0.027) OTU8, OTU9 (0.037)

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

SHORT BARCODES FROM TYPES CLARIFY SPECIES STATUS 977

and character analyses associated both it and E. berndtiel-

la with OTU7 (Table 9). All other type specimens were

also associated with OTUs. In fact, eight holotypes were

grouped within OTU12, while six types were associated

with OTU8 with one additional holotype (E. skulei) as a

close sister lineage. The results of the BLOG analyses pro-

duced discordant outcomes as they associated E. skulei

with OTU7. The barcode results associated two sets of

three to four holotypes with OTU10 and OTU15. Three

other types (E. tribertiella, E. bazaella and E. louiseae) pos-

sessed identical barcode sequences and formed the sister

lineage for OTU9. Two other holotypes were associated

with an OTU (E. oukaimedenensis with OTU1; E. gerdmari-

tella with OTU13) as discussed earlier.

Correspondence of type morphology with OTUs

The holotypes of E. moroccoensis, E. blancella, E. minuscul-

ella and the recent specimens of OTU18 appear indistin-

guishable by morphology, but as E. moroccoensis had a

distinctive barcode, it may represent a cryptic species.

Morphology supports the association of the E. casascoensis

holotype with OTU7 that also includes the holotype of

E. berndtiella, with no character found to differentiate the

recognition of E. casascoensis from the widespread OTU7.

The type of E. gerdmaritella, which was differently placed

in the tree-based methods, shows no morphological dif-

ferences from specimens in OTU13. The OTUs with sev-

eral matching holotypes (e.g. 8, 10, 12, 15) each include

specimens that are indistinguishable morphologically.

The same holds true with the holotypes associated with

these OTUs as they also do not differ from each other in

any other way except on the basis of the wing venation

traits whose value as species-diagnostic trait is question-

able (Albrecht & Kaila 1997). Hence, the results from mor-

phology correspond closely with those from barcodes.

Discussion

This study was initially motivated by the uncertain tax-

onomy of the Elachista dispunctella complex which likely

derives from two factors. First, many Elachista species

show subtle morphological diversification (Kaila 2011c;

Mutanen et al. 2013). Second, prior taxonomic work has

sometimes created an apparent proliferation of names

(cf. Albrecht & Kaila 1997). Our study asked whether

DNA barcodes from type specimens could help to clarify

the true of species boundaries in the E. dispunctella com-

plex. To accomplish this, we employed a procedure

which combined various unsupervised and supervised

methods together with the inspection of morphological

characters. This approach was necessary due to the short

length of the DNA barcodes of the type specimens as the

unsupervised analysis of all sequences was not possible.

Following this procedure, 20 OTUs were delineated

based on DNA barcodes (including the OTU of E. moroc-

coensis), and 17 of these OTUs were still recognized after

morphological study. These 17 OTUs will be prioritized

for recognition as species in a future taxonomic revision

unless evidence supporting the distinctiveness of addi-

tional OTUs emerges during the revisionary work. The

status of other apparently cryptic species should also be

examined with further molecular methods.

These moths are small (10 mm wingspan), and the

abdomens of all types are permanently mounted so they

are unavailable for DNA extraction. As a result, little tis-

sue material from types was available for analysis, but

we recovered partial barcode sequences from the holo-

types of 33 species, making it possible to see whether

Table 7 Results of BOLD ID engine. Type sequences were

searched against all barcode records on BOLD with a minimum

sequence length of 500 bp

Types

BOLD ID engine

(# matching

sequences)

Result with

highest

similarity (%)

Elachista oukaimedenensis OTU1 (3) 97.85

E. berndtiella OTU7 (7) 100

E. casascoensis OTU7 (2) 99.38

E. multipunctella OTU8 (7) 99.38

E. karsholti OTU8 (16) 100

E. povolnyi N/A

E. intrigella OTU8 (7) 100

E. imbi OTU8 (7) 100

E. nilspederi OTU8 (7) 100

E. skulei OTU8 (7) 98.15

E. bazaella OTU9 98.92

E. louiseae N/A

E. tribertiella OTU9 98.89

E. baldizzonella OTU10 (7) 100

E. veletaella N/A

E. toveella OTU10 (7) 100

E. glaseri OTU12 (20) 100

E. olemartini OTU12 (20) 100

E. senecai OTU12 (20) 100

E. rikkeae OTU12 (20) 100

E. wadielhiraensis OTU12 (1) 100

E. bengtssoni OTU12 (20) 100

E. rissaniensis OTU12 (20) 100

E. michelseni N/A

E. gerdmaritella OTU13 (20) 98.92

E. vanderwolfi N/A

E. varensis OTU15 (9) 100

E. hispanica OTU15 (9) 100

E. occidentella OTU15 (9) 100

E. anitella OTU17 (2) 100

E. blancella OTU18 (7) 99.38

E. minusculella N/A

E. moroccoensis E. moroccoensis* (1) 100

OTU, operational taxonomic unit.

*Type specimen associated with itself.

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

978 M. MUTANEN ET AL .

Table

8ResultsoffourBLOGan

alyses(654,162,

93,5

4bp).ThetypeofE.moroccoensisisexcluded

from

thistable

asitonly

associated

withitselfin

allan

alyses

Seq

uen

celength

(bp)

654

162

9354

#corr.

%corr.

#wrong

#n.c.

#corr.

%corr.

#wrong

#n.c.

#corr.

%corr.

#wrong

#n.c.

#corr.

%corr.

#wrong

#n.c.

OTU1

00

01

1100

00

1100

00

1100

00

OTU2

00

00

00

00

00

00

00

00

OTU3

00

00

00

00

00

00

00

00

OTU4

00

00

00

00

00

00

00

00

OTU5

00

00

00

00

00

00

00

00

OTU6

00

00

00

00

00

00

00

00

OTU7

00

02

2100

00

2100

00

2100

00

OTU8

00

07

571.43

11

571.43

11

685.71

10

OTU9

3100

00

3100

00

3100

00

3100

00

OTU10

00

03

3100

00

3100

00

3100

00

OTU11

00

00

00

00

00

00

00

00

OTU12

00

08

8100

00

8100

00

8100

00

OTU13

00

01

1100

00

1100

00

1100

00

OTU14

00

00

00

00

00

00

00

00

OTU15

00

04

125

03

375

01

4100

00

OTU16

00

00

00

00

00

00

00

00

OTU17

00

01

1100

00

1100

00

1100

00

OTU18

00

02

150

01

150

01

150

01

#corr.,number

ofcorrectlyclassified

elem

ents;%

corr.,percentageofcorrectlyclassified

elem

ents;#

wrong,number

ofwrongly

classified

elem

ents;#

n.c.,number

ofunclassified

ele-

men

ts;OTU,operational

taxonomicunit.

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

SHORT BARCODES FROM TYPES CLARIFY SPECIES STATUS 979

their sequences showed congruence with one or more of

the OTUs delineated in the study of recent specimens.

Because the number of OTUs recovered from modern

specimens was less than the number of type specimens

analysed, one would expect many sequences from type

specimens not to find a match if each described species

is distinct. In fact, there was only a single case of such

divergence, the holotype of E. moroccoensis represented a

unique OTU, with 4.5% divergence from the closest

OTU. As a consequence, the validity of E. moroccoensis as

separate species is supported by its sequence divergence.

By contrast, there were numerous cases where the bar-

code sequence from several type specimens matched a

particular OTU with the most extreme case involving

eight type specimens associated with OTU12 (cf. Fig. 3).

This result suggests that prior taxonomic work has led to

considerable oversplitting, a conclusion fortified by the

close agreement between morphological results and

those from barcode analysis (cf. Table 4). We conclude

that the sequences recovered, even the shortest ones, are

a valuable enabler for a comprehensive integrative taxo-

nomic revision of the group. Because such a revision will

require the reassessment of all 64 nominal species, it is

beyond the scope of the present investigation.

Evaluation of the procedure employed

Due to the short length of the DNA barcodes recovered

from type specimens, we conducted an initial phase of

OTU delineation prior to associating types to these

OTUs. This approach served two goals: re-examining

species belonging to the E. dispunctella complex and pro-

viding clearly defined groups to be used in the type asso-

ciation phase. In the study of Puillandre et al. (2011), type

barcodes were associated employing tree-based methods

(NJ and BI) without a separate delineation phase. This

simplifies the procedure, but most of the benefits derived

from DNA-based methods for species delineation meth-

ods (e.g. accuracy, objectivity and repeatability) are not

utilized. As methods and criteria used in the delineation

phase were detailed in a previous study (Kekkonen &

Hebert 2014), further discussion on this approach would

not offer any new aspects and is thus excluded.

The majority (15 of 19) of the putative species (OTUs)

delineated through barcode analysis were congruent

with morphological characters, and all four cases of dis-

cordance involved cases of splits recognized by DNA

barcodes where the groups showed no morphological

divergence (Fig. 3). The few cases of discordance indi-

cate that most of our data were free from the potential

problems caused by single-locus DNA. The most dubi-

ous result of barcode-based delineation was the division

of OTU4, a result that was likely an artefact of small sam-

ple sizes. The same factor may explain the discordant

result for OTUs 9 and 10, as all three sequence-based

delineation methods recognized OTU9. The most con-

vincing evidence for cryptic species was offered by

E. moroccoensis as its full-length barcode was always sep-

arated from the other sequences.

The tree- and distance-based methods employed for

type association generally produced results concordant

with those from morphology. The results were also

mostly congruent between different DNA-based meth-

ods, indicating that tree- and distance-based methods

can be employed even with very short sequences, such

Table 9 Type association based on DNA barcodes. Numbers in

cells correspond to the operational taxonomic unit (OTU) where

each method linked a given type specimen. Discordant results

are marked in bold

Types NJ ML BI

Pw.

dist.

BOLD

ID BLOG

Elachista

oukaimedenensis

1* 1 1* 1 1 1

E. berndtiella 7 7 7 7 7 7

E. casascoensis 8,9 9,10 9,10 7 7 7

E. multipunctella 8 8 8 8 8 8

E. karsholti 8 8 8 8 8 8

E. povolnyi 8 8 8 8 N/A 8

E. intrigella 8 8 8 8 8 8

E. imbi 8 8 8 8 8 8

E. nilspederi 8 8 8 8 8 8

E. skulei 8* 8* 8* 8 8 7

E. bazaella 9* 9* 9* 9 9 9

E. louiseae 9* 9* 9* 9 N/A 9

E. tribertiella 9* 9* 9* 9 9 9

E. baldizzonella 10 10 10 10 10 10

E. veletaella 10 10 10 10 N/A 10

E. toveella 10 10 10 10 10 10

E. glaseri 12 12 12 12 12 12

E. olemartini 12 12 12 12 12 12

E. senecai 12 12 12 12 12 12

E. rikkeae 12 12 12 12 12 12

E. wadielhiraensis 12 12 12 12 12 12

E. bengtssoni 12 12 12 12 12 12

E. rissaniensis 12 12 12 12 12 12

E. michelseni 12 12 12 12 N/A 12

E. gerdmaritella 16* 13 13 13 13 13

E. vanderwolfi 15 15 15 12,15 N/A 15

E. varensis 15 15 15 15 15 15

E. hispanica 15 15 15 15 15 15

E. occidentella 15 15 15 15 15 15

E. anitella 17 17 17 17 17 17

E. blancella 18 18 18 18 18 18

E. minusculella 18 18 18 18 N/A N/A

E. moroccoensis None None None N/A N/A N/A

NJ, Neighbour-Joining; ML, maximum likelihood; BI, Bayesian

inference; Pw. dist., pairwise distances; BOLD ID, BOLD Identi-

fication system; BLOG, BLOG analysis with the sequence length of

54 bp.

*Placed as a sister lineage.

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

980 M. MUTANEN ET AL .

as those often recovered from type specimens. However,

our results revealed some drawbacks in analysing short

sequences with certain methods. Tree-based methods,

such as neighbour-joining and maximum likelihood,

have the pitfall that they can place a short sequence on a

distinct node even if it is identical with several different

full-length sequences. This problem can be averted by

determining whether each holotype is equidistant from

several full-length barcodes or just one. This issue was

detected for the identical sequences of E. blancella and

E. minusculella which were differently placed, although

they were still associated with the same OTU (OTU18)

(Figs S12 and S13, Supporting information). Bayesian

inference analysis avoided this error as it placed the two

type specimens in the same cluster (Fig. S14, Supporting

information). Also, the placement of E. casascoensis,

which was associated with OTU7 by distance- and char-

acter-based methods and morphology, was rather unsta-

ble in tree-based analyses. Comparison of pairwise

distances can yield a biased outcome when overlap

between the target sequence and reference sequences is

partial. Therefore, in critical cases, such reference

sequences should be excluded from analysis. This prob-

lem was detected in one case (E. vanderwolfi).

BLOG appeared to be particularly sensitive to variation

in sequence length because the analysis of the dataset

with both long and short sequences resulted in only

three correctly assigned types. By contrast, when the

length of all sequences was trimmed to 162 bp, the num-

ber of correctly assigned types rose to 26 with the short-

est sequences (54 bp) producing the highest number of

correct matches. This indicates that BLOG should only be

used with DNA sequences of equal or very similar

length. In addition, it was the only method (out of six)

that delivered an unambiguous result (i.e. type barcode

is either associated with a particular OTU or not), while

the results from other methods are more subjective. This

feature makes BLOG, or any other supervised method

based on diagnostic characters, recommended provided

that the sequences are either similar in length or are

trimmed to meet this requirement.

Because of the increasing efforts being directed

towards the barcoding of type specimens, often yielding

only short sequences, the potential pitfalls of both tree-

building methods and distance matrices should be con-

sidered. The tendency of neighbour-joining analysis to

generate fully-resolved trees (up to the level of entirely

identical sequences) even when this is not supported by

the data is an obvious problem.

Future of type barcoding

Recent progress in the recovery of DNA sequences pro-

vides hope that nondestructive sampling (e.g. Hunter

et al. 2008) of type specimens will soon become a routine

part of establishing a stable nomenclature, especially in

groups that are currently intractable because of the poor

condition of type specimens or the close morphological

similarity of species. Our results support earlier conclu-

sions that even short segments of the barcode region usu-

ally provide enough information to associate type

material with other specimens (Meusnier et al. 2008; Lees

et al. 2010; Shokralla et al. 2011). In addition, different

methods are available to acquire the whole barcode. For

instance, primer walking can recover the entire region

through sequencing several overlapping fragments, an

approach already used for moths (Hausmann et al.

2009a,b; Wilson et al. 2010; Rougerie et al. 2012), and fruit

flies (van Houdt et al. 2010). For example, Strutzenberger

et al. (2012) recovered complete barcodes from 95% of

the abdomens that they sampled from 79- to 157-year-

old Eois moths. Unfortunately, because all holotypes

examined in this study lacked an abdomen, it was neces-

sary to analyse just a single leg, providing much lower

concentrations of DNA.

Techniques to recover DNA barcodes from old speci-

mens are developing rapidly. It is likely that both San-

ger-based and next-generation sequencing techniques

will soon recover DNA sequences from older and more

degraded specimens than is routine today. This prospect

opens new opportunities for resolving taxonomic prob-

lems that require a better understanding of the identity

of the name-bearing specimen. In taxonomy, type speci-

mens are central in associating taxa with names. As

shown in this study, DNA barcoding can help to estab-

lish this linkage in a powerful way. Because DNA extrac-

tion can now be performed in a nondestructive manner,

there is no basis for opposition to the analysis of type

specimens. We find it inconsistent that the policies of

some museums do not permit tissue sampling from type

material for DNA analysis, although other far more

destructive investigations (e.g. genital dissection) are

permitted.

Species delimitation is inherently subjective, reflecting

the variety of species concepts of species and the difficul-

ties associated with deciding the status of allopatric pop-

ulations. Such situations are problematic in data

management, maintaining checklists and keeping track

of new information of species distributions, in a wide

variety of databases of importance for numerous applica-

tions. In less-studied groups of organisms, new samples

may change the prevailing taxonomic perspective, for

instance by making it possible to better quantify intra- or

interspecific variation which may shift conclusions on

species boundaries. Sometimes, poorly justified

approaches to distinguish taxa can cause overlumping or

oversplitting of taxa. Both of these phenomena cause

problems: lumping obscures true diversity and splitting

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

SHORT BARCODES FROM TYPES CLARIFY SPECIES STATUS 981

overestimates it. Both lumping and splitting hamper the

accurate identification of specimens biasing the interpre-

tation of variation in diagnostic features. In the species

complex considered in this study, oversplitting has led

to the situation where no specimen in this group of Euro-

pean moths, generally assumed to be well known, could

be correctly identified since 1992. A reliable morphology-

based taxonomic revision has been extremely difficult to

advance because of the insurmountable difficulties in

understanding the identity or differentiation of many of

the holotypes. DNA barcodes greatly helped us to deter-

mine the morphological features which play an impor-

tant role in species discrimination vs. those that are

likely to merely reflect intraspecific variation. The proce-

dure employed here has clarified the taxonomic status of

many component taxa so that an escape from this taxo-

nomic quagmire now appears possible. Although this

case is extreme, it is certainly not unique. The taxonomy

of many groups of animals with limited or poorly under-

stood morphological differentiation may be repaired

with the adoption of DNA-based tools, optimally in com-

bination with other sources of taxonomic information.

Acknowledgements

We thank Bengt Bengtsson, Robert Heckford, Peter Huemer,

Jari Junnilainen, Jari-Pekka Kaitila, Ole Karsholt, Kari Nuppo-

nen, Timo Nupponen, Nikolay Savenkov, Jukka Tabell and

Zdenko Tokar for providing specimens for analysis. We cor-

dially thank Sabine Gaal-Haszler, Ole Karsholt and Erik Van

Nieukerken for permitting DNA barcode analyses on type spec-

imens under their curation. We are grateful to Sujeevan Ratnas-

ingham for performing the RESL stand-alone analysis and

Emanuel Weitschek for providing advice on the use of BLOG.

We deeply appreciate support from the Gordon and Betty

Moore Foundation which provided the technical support and

infrastructure needed to carry out this project. We are grateful

for funding from the Government of Canada through Genome

Canada and the Ontario Genomics Institute to the International

Barcode of Life Project which enabled the Canadian Centre for

DNA Barcoding to carry out the sequence analysis. We also

thank the Ontario Ministry of Research and Innovation for

funding the ongoing development of BOLD. This work was

supported by Research Foundation of the University of Hel-

sinki, Finnish Concordia Fund, and the Ella and Georg Ehrnro-

oth Foundation to MK. We also thank the Kone Foundation

and the Finnish Cultural Foundation for supporting the Finnish

Barcode of Life Project. Finally, we cordially thank Axel Haus-

mann, Tommi Nyman and three anonymous referees for

numerous useful revisionary suggestions that significantly

improved our study.

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M.M. and L.K. contributed the original idea; M.K., M.M.

and L.K. designed the research; M.K., M.M., L.K., S.P.

and P.H. performed the research; P.H. contributed

reagents and analytical tools; M.K., M.M. and L.K. analy-

sed the data; M.M., M.K., L.K., S.P. and P.H. wrote the

manuscript.

Data accessibility

DNA sequences: GenBank Accession nos are provided in

Table S3 (Supporting Information).

The full specimen and sequence data are available

through a public dataset DS-ELADIS at BOLD (www.bold-

systems.org), DOI: http://dx.doi.org/10.5883/DS-ELADIS.

The following data are available online in Supporting

Information files S1–S16: Checklist of species of Elachista

dispunctella complex, type specimen information, speci-

men data, ABGD source code, XML files for executing

BEAST, R code, schematic depiction of male and female

genitalia of E. dispunctella complex, FASTA MSA for

OTU delineation and type association, a graph showing

the number of OTUs plotted against P-values, neigh-

bour-joining, maximum likelihood and Bayesian trees

and BLOG logic classification formulas.

Supporting Information

Additional Supporting Information may be found in the online

version of this article:

Appendix S1 Checklist of species of Elachista dispunctella com-

plex

Appendix S4 ABGD source code

Appendix S5 XML file for executing BEAST (OTU delineation)

Appendix S6 R code

Appendix S9 XML file for executing BEAST (type association)

Appendix S10 FASTA MSA for OTU delineation

Appendix S15 FASTA MSA for type association

Appendix S16 BLOG logic classification formulas

Figure S7 Schematic depiction of male genitalia of E. dispunctella

complex

Figure S8 Schematic depiction of female genitalia of E. dispunc-

tella complex

Figure S11 The number of OTUs plotted against P-values

Figure S12 NJ tree

Figure S13 ML tree

Figure S14 Bayesian tree

Table S2 Type specimen information of species of E. dispunctella

complex

Table S3 Specimen data

© 2014 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

984 M. MUTANEN ET AL .